[Driving mechanisms and research challenges of phytoplankton community structure shifts in the coastal China seas.].

Authors

Li-Na An, Zhi-Feng Yan, Xiu-Lei Zhang, Hang Yin, Xin Liu, Jun Sun, Bang-Qin Huang, Gui-Rui Yu

Published in

Ying yong sheng tai xue bao = The journal of applied ecology. Volume 37. Issue 9. Pages 3066-3078. Sep 18, 2026.

Abstract

The coastal China seas are a representative marine region jointly influenced by global climate change and human activities. As primary producers in marine ecosystems, phytoplankton community structure strongly influences ecosystem stability. Here, we summarized the spatiotemporal patterns, driving mechanisms, and modeling approaches of phytoplankton community structure shifts in the coastal China seas. Phytoplankton communities exhi-bit pronounced regional heterogeneity. In nearshore waters, eutrophication and changing nutrient regimes have generally weakened diatom dominance and increased the relative abundance of dinoflagellates and small-sized phytoplankton, accompanied by an increased risk of harmful algal blooms. In offshore and shelf-edge regions, ocean warming and enhanced stratification have promoted a trend toward smaller phytoplankton in size classes. Transitional zones are regulated by the combined effects of water-mass exchange, frontal dynamics, estuarine plumes, and upwelling currents, which collectively drive enhanced spatiotemporal variations in phytoplankton community structure. Community succession is driven by interactions among resource supply, environmental filtering, and biological processes, with temperature, light, nutrient stoichiometry, and microzooplankton grazing playing key roles. Current predictions of phytoplankton community dynamics still face challenges, including limited understanding of ecological mechanisms, simplified functional-group representations, difficulties in parameterizing key processes, and inadequate resolution of mesoscale and submesoscale dynamics. Future studies should strengthen long-term observations and mechanism-based experiments, and develop multi-scale coupled models integrating multiple nutrient forms, high-resolution physical processes, and machine-learning approaches to improve predictions of phytoplankton community dynamics and ecosystem responses.

PMID:
42864096
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.

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